The increasing adoption of renewable energy sources burdens electric grids with intermittent fluctuations in power generation. Wind generators may fluctuate significantly over a timescale of a few minutes to an hour and solar photovoltaic (PV) generation fluctuates on timescales of a few minutes or shorter. Not only must grid operators precisely balance supply with load, they must ensure voltage quality and stability, incurring additional costs by procuring ancillary services (Ela, Erik et al. “Effective ancillary services market designs on high wind power penetration systems” in Power and Energy Society General Meeting, 2012 IEEE, pp. 1-8. IEEE, [2012]). These are impaired by rapidly fluctuating sources of power and must be compensated by costly and limited resources such as FACTS devices, energy storage, switchable capacitor networks, tap-changing transformers and rapid-response generation operating inefficiently at partial capacity, held for regulation reserve.
The Utility Variable Integration Group was established to study the problems incurred by intermittent resources. The US National Renewable Energy Laboratory as well other studies indicate cost burdens due to intermittency ranging from several $/megawatt-hour (MWh) to over $10/MWh depending on the percentage of energy produced by variable generation. One conclusion is that forecasting changes in production will aid scheduling and lower costs. The US Federal Energy Regulatory Commission recently issued order 764, requiring wholesale variable energy generation facilities to submit meteorological forecast and operational data to grid operators in order to facilitate system management. Energy storage is now used at some sites to mitigate power fluctuations, however this is often so costly as to inhibit economic feasibility.
A method of forecasting irradiance and consequently PV energy production using a spatially extended array of irradiance sensors is disclosed by Bosch et al. (Solar Energy, vol. 87 [2013] p. 196). An alternative method uses a sky camera with a fish-eye lens at a central location images clouds to calculate a forecast based on cloud motions (Chow et al. Solar Energy vol. 85 [2011] p. 2881; Marquez and Coimbra, Solar Energy vol. 91 [2013] p. 327).